I have conducted a double-bounded contingent valuation study and analysed it in Stata with Lopez-Feldmann's (2012) doubleb command. The resulting coefficients are confusing me. I am not sure whether they are standardised or unstandardised and this is not helping with my conclusions.

I know that probit regression usually returns standardised coefficients, so I would not be able to say that a 1 unit increase in the IV results in a (coef.) increase in the DV. However, since the Lopez-Feldmann model is a variation of probit regression and his explanation of it shows the coefficients to be unstandardised(see below), I have interpreted my results in this way. But now I am uncertain and need to defend my results on Wednesday.

From Lopez-Feldmann (2012) - https://mpra.ub.uni-muenchen.de/41018/2/MPRA_paper_41018.pdf

The last output before the references shows the difference between male and female WTP to be exactly that of the coefficient : so this would mean the coefficient is standardised, right?

I have now interpreted all my coefficients (some linear, some categorical some nominal) as unstandardised. Is this correct? If so, how can I argue that it is correct that they are unstandardised although they are usually standardised in probit?

Here is my Stata output:

initial: log likelihood = -<inf> (could not be evaluated)

feasible: log likelihood = -6032.3338

rescale: log likelihood = -466.69701

rescale eq: log likelihood = -464.61633

Iteration 0: log likelihood = -464.61633 (not concave)

Iteration 1: log likelihood = -447.67555 (not concave)

Iteration 2: log likelihood = -410.05117 (not concave)

Iteration 3: log likelihood = -402.94301 (not concave)

Iteration 4: log likelihood = -399.09683

Iteration 5: log likelihood = -375.40299

Iteration 6: log likelihood = -364.54334

Iteration 7: log likelihood = -361.66365

Iteration 8: log likelihood = -361.64446

Iteration 9: log likelihood = -361.64445

Number of obs = 137

Wald chi2(8) = 53.00

Log likelihood = -361.64445 Prob > chi2 = 0.0000

------------------------------------------------------------------------------

| Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

Beta |

age | -.3450866 .1649973 -2.09 0.036 -.6684753 -.0216978

gender | 7.255382 3.653988 1.99 0.047 .0936969 14.41707

income | -.0000227 .0020633 -0.01 0.991 -.0040666 .0040212

org | 2.666961 1.273459 2.09 0.036 .1710266 5.162896

qualU | 19.41732 3.6545 5.31 0.000 12.25463 26.58001

self | -.0649317 .1229359 -0.53 0.597 -.3058816 .1760183

env | .2843504 .1289765 2.20 0.027 .0315611 .5371396

soc | .1056503 .1646891 0.64 0.521 -.2171343 .4284349

_cons | 44.36785 11.70759 3.79 0.000 21.4214 67.3143

-------------+----------------------------------------------------------------

Sigma |

_cons | 16.75996 1.485025 11.29 0.000 13.84937 19.67056

------------------------------------------------------------------------------

First-Bid Variable: bid1

Second-Bid Variable: bid2

First-Response Dummy Variable: answer1

Second-Response Dummy Variable: answer2

So as an example: I said that an increase from one category of organic buying frequency to the next (org) causes a 2.67 percentage point increase in Willingness to Pay.

Any help would be very much appreciated

Thanks

Joe